In computer science education, test cases are an integral part of programming
assignments since they can be used as assessment items to test students'
programming knowledge and provide personalized feedback on student-written
code. The goal of our work is to propose a fully automated approach for test
case generation that can accurately measure student knowledge, which is
important for two reasons. First, manually constructing test cases requires
expert knowledge and is a labor-intensive process. Second, developing test
cases for students, especially those who are novice programmers, is
significantly different from those oriented toward professional-level software
developers. Therefore, we need an automated process for test case generation to
assess student knowledge and provide feedback. In this work, we propose a large
language model-based approach to automatically generate test cases and show
that they are good measures of student knowledge, using a publicly available
dataset that contains student-written Java code. We also discuss future
research directions centered on using test cases to help students.

我们提出了一种基于大型语言模型的自动测试用例生成方法，证明它们是衡量学生知识的良好指标，使用了一个包含学生编写的 Java 代码的公开数据集，并讨论了以测试用例帮助学生为中心的未来研究方向。

利用大型语言模型进行计算机科学教育中学生代码引导的测试用例生成

Using Large Language Models for Student-Code Guided Test Case Generation  in Computer Science Education

Accurate modeling of student knowledge is essential for large-scale online
learning systems that are increasingly used for student training. Knowledge
tracing aims to model student knowledge state given the student's sequence of
learning activities. Modern Knowledge tracing (KT) is usually formulated as a
supervised sequence learning problem to predict students' future practice
performance according to their past observed practice scores by summarizing
student knowledge state as a set of evolving hidden variables. Because of this
formulation, many current KT solutions are not fit for modeling student
learning from non-assessed learning activities with no explicit feedback or
score observation (e.g., watching video lectures that are not graded).
Additionally, these models cannot explicitly represent the dynamics of
knowledge transfer among different learning activities, particularly between
the assessed (e.g., quizzes) and non-assessed (e.g., video lectures) learning
activities. In this paper, we propose Transition-Aware Multi-activity Knowledge
Tracing (TAMKOT), which models knowledge transfer between learning materials,
in addition to student knowledge, when students transition between and within
assessed and non-assessed learning materials. TAMKOT is formulated as a deep
recurrent multi-activity learning model that explicitly learns knowledge
transfer by activating and learning a set of knowledge transfer matrices, one
for each transition type between student activities. Accordingly, our model
allows for representing each material type in a different yet transferrable
latent space while maintaining student knowledge in a shared space. We evaluate
our model on three real-world publicly available datasets and demonstrate
TAMKOT's capability in predicting student performance and modeling knowledge
transfer.

本文提出了一种多环境知识追踪模型（TAMKOT），它可以同时对学生所处的学习环境和学生的知识状态建模，并能表示不同学习材料之间的知识转移。在真实世界的数据集上验证实验表明，TAMKOT 能够很好地预测学生的表现并建模知识转移。